With the release of the Moving and Stationary Target Acquision Recognition (MSTAR) public data set, high quality Synthetic Aperture Radar (SAR) imagery of military ground vehicles has been made accessible to the entire research community. Furthermore, standard methods for evaluating classifier results on this data set have been created and released. Using these tools, we reconsider a previously contracted application of AND Corporation's Holographic/Quantum Neural Technology (HNeT) classifier, performing brief analyses of the way HNeT selects features for Automatic Target Recognition (ATR) purposes, the methodology used in the contract and their results, as well as obtaining new results that comply with the MSTAR standard evaluation criteria. These results provide measures of performance for the HNeT classifier using Confusion Matrices and Receiver Operating Characteristic (ROC) curves, that are used to compare with the open literature performance of the MSTAR baseline and two other classifiers, from which we conclude that HNeT outperforms the other three and provides improved ATR.